Reproducing static and dynamic biodiversity patterns in tropical forests: the critical role of environmental variance

Abstract

Ecological communities are subjected to stochasticity in the form of demographic and environmental variance. Stochastic models that contain only demographic variance (neutral models) provide close quantitative fits to observed species-abundance distributions (SADs) but substantially underestimate observed temporal species-abundance fluctuations. To provide a holistic assessment of whether models with demographic and environmental variance perform better than neutral models, the fit of both to SADs and temporal species-abundance fluctuations at the same time has to be tested quantitatively. In this study, we quantitatively test how closely a model with demographic and environmental variance reproduces total numbers of species, total abundances, SADs and temporal species-abundance fluctuations for two tropical forest tree communities, using decadal data from long-term monitoring plots and considering individuals larger than two size thresholds for each community. We find that the model can indeed closely reproduce these static and dynamic patterns of biodiversity in the two communities for the two size thresholds, with better overall fits than corresponding neutral models. Therefore, our results provide evidence that stochastic models incorporating demographic and environmental variance can simultaneously capture important static and dynamic biodiversity patterns arising in tropical forest communities.

Stability of two-species communities: Drift, environmental stochasticity, storage effect and selection

“…Again, species go extinct at a certain rate, now determined by both demographic and environmental stochasticity, and biodiversity reflects the balance between extinction and speciation/migration rates. This model has been shown to fit quite nicely the static and dynamic characteristics of a (local) community of tropical trees [16,18]; both the species abundance distribution and the abundance variations are similar to the predictions of the model as obtained from numerical simulations.…”

Reinterpreting maximum entropy in ecology: a null hypothesis constrained by ecological mechanism

“…; Fung et al . ). At this point, we do not have a corresponding maximum entropy baseline for these models.…”

Temporal population variability in local forest communities has mixed effects on tree species richness across a latitudinal gradient

“…Our modelling analyses also help to shed light on the general question of how complex a dynamic, mechanistic model needs to be to accurately capture temporal population variability in an ecological community. Drift‐only models with constant community sizes are inadequate in most cases (Chisholm & O’Dwyer ; Chisholm et al ; Kalyuzhny et al ; Fung et al ). Our analyses suggest that adding temporal variation in recruitment rates (Chisholm et al ; Kalyuzhny et al ; Fung et al ) and community sizes is generally sufficient to accurately capture temporal population variability.…”

“…Drift‐only models with constant community sizes are inadequate in most cases (Chisholm & O’Dwyer ; Chisholm et al ; Kalyuzhny et al ; Fung et al ). Our analyses suggest that adding temporal variation in recruitment rates (Chisholm et al ; Kalyuzhny et al ; Fung et al ) and community sizes is generally sufficient to accurately capture temporal population variability. But in the six tree communities where our mechanistic model substantially under‐ or over‐estimated observed temporal population variability, additional mechanisms are required to get a better approximation of the true temporal dynamics.…”

“…Therefore, measured the temporal correlation in environmental conditions. The model we used captured the effects of deterministic selection (arising from fitness differences among species in a given environment; Vellend ), stochastic ecological drift (Hubbell ) and stochastic local‐scale environmental fluctuations over time (Lande ; Chisholm et al ; Kalyuzhny et al , ; Fung et al, ). Fig.…”